Hi Nick,
The question was not how to report measurements, but how to deal with
the simulation from the model, that was likely developed on the data set
where BQLs were either ignored or treated as BQLs (e.g., set to 0, set
to BQL/2, treated with M3: in the best case, the exact method can be
found in the paper).
Not sure that "honest" and "dishonest" belongs here any way, there are
many ways to solve the problem, and it is not helpful to label as
"dishonest" the way that does not coincide with the one that you prefer.
Best!
Hope it is safe and healthy on your side of the globe (where people are
still go around upside down :) )
Best,
Leonid
On 6/2/2020 3:46 PM, Nick Holford wrote:
Hi Nyein,
For drug concentrations the additive error model assumes that the background
noise is random with mean zero when the drug concentration is truly zero. In
the real world there is always background noise for measurements which means
that real measurements can appear to be a negative concentration even though
the true concentration is zero. Simulations that simulate negative
concentrations are therefore more realistic than those that ignore reality and
are reported as censored measurement values.
The honest thing to do is to report measurements as they are. The dishonest
thing is to report real measurements as below some arbitrary limit of
quantification. There are numerous papers which describe the bias arising from
dishonest reporting of real measurements and work arounds if you have to deal
with this kind of scientific fraud e.g.
Beal SL. Ways to fit a PK model with some data below the quantification limit.
Journal of Pharmacokinetics & Pharmacodynamics. 2001;28(5):481-504.
Duval V, Karlsson MO. Impact of omission or replacement of data below the limit
of quantification on parameter estimates in a two-compartment model. Pharm Res.
2002;19(12):1835-40.
Ahn JE, Karlsson MO, Dunne A, Ludden TM. Likelihood based approaches to
handling data below the quantification limit using NONMEM VI. J Pharmacokinet
Pharmacodyn. 2008;35(4):401-21.
Byon W, Fletcher CV, Brundage RC. Impact of censoring data below an arbitrary
quantification limit on structural model misspecification. J Pharmacokinet
Pharmacodyn. 2008;35(1):101-16.
Senn S, Holford N, Hockey H. The ghosts of departed quantities: approaches to
dealing with observations below the limit of quantitation. Stat Med.
2012;31(30):4280-95.
Keizer RJ, Jansen RS, Rosing H, Thijssen B, Beijnen JH, Schellens JHM, et al.
Incorporation of concentration data below the limit of quantification in population
pharmacokinetic analyses. Pharmacology research & perspectives.
2015;3(2):10.1002/prp2.131
Best wishes,
Nick
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
office:+64(9)923-6730 mobile:NZ+64(21)46 23 53 FR+33(6)62 32 46 72
email: n.holf...@auckland.ac.nz
http://holford.fmhs.auckland.ac.nz/
http://orcid.org/0000-0002-4031-2514
Read the question, answer the question, attempt all questions
-----Original Message-----
From: owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> On Behalf Of
Bill Denney
Sent: Tuesday, 2 June 2020 8:30 PM
To: Nyein Hsu Maung <nyeinhsumaung2...@gmail.com>; nmusers@globomaxnm.com
Subject: RE: [NMusers] Negative concentration from simulation
Hi Nyein,
Negative concentrations can be expected from simulations if the model includes additive
residual error. I assume that you mean additive and proportional error when you say
"combined error model". If the error structure does not include additive
error, then we'd need to know more.
How you will handle them in analysis depends on the goals of the analysis.
Usually, you will either simply set negative values to zero or set all values
below the limit of quantification to zero.
Thanks,
Bill
-----Original Message-----
From: owner-nmus...@globomaxnm.com <owner-nmus...@globomaxnm.com> On Behalf Of
Nyein Hsu Maung
Sent: Tuesday, June 2, 2020 2:13 PM
To: nmusers@globomaxnm.com
Subject: [NMusers] Negative concentration from simulation
Dear NONMEM users,
I tried to simulate a new dataset by using a previously published pop pk model.
Their model was described by combined error model for residual variability. And
after simulation, I have obtained two negative concentrations. I would like to
know if there is any proper way to handle those negative concentrations or if
there are some codings to prevent gaining negative concentrations. Thanks.
Best regards,
Nyein Hsu Maung